Many business cases exploiting big data have been realised in recent years; Twitter,
LinkedIn, and Facebook are examples of companies in the social networking domain. Other …

Data stream processing (DSP) has emerged over the years as the reference paradigm for
the analysis of continuous and fast information flows, which often have to be processed with …

Serving ML prediction pipelines spanning multiple models and hardware accelerators is a
key challenge in production machine learning. Optimally configuring these pipelines to meet …

Stream Processing (SP) has evolved as the leading paradigm to process and gain value
from the high volume of streaming data produced, eg, in the domain of the Internet of Things …

Big Data analytics has recently gained increasing popularity as a tool to process large
amounts of data on-demand. Spark and Flink are two Apache-hosted data analytics …

Many Big Data applications in science and industry have arisen, that require large amounts
of streamed or event data to be analyzed with low latency. This paper presents a reactive …

Stream processing is an emerging paradigm to handle data streams upon arrival, powering
latency-critical application such as fraud detection, algorithmic trading, and health …

Prediction serving systems are designed to provide large volumes of low-latency inferences
machine learning models. These systems mix data processing and computationally …

Modern stream processing systems need to process large volumes of data in real-time.
Various stream processing frameworks have been developed and messaging systems are …

Stacked memory modules are likely to be tightly integrated with the processor. It is vital that
these memory modules operate reliably, as memory failure can require the replacement of …